Evidence map›Paper›PMID 37063926›Full record

ArticleFrontiers in immunology2023

Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets.

Jiahui Jin, Yifan Liu, Qinyu Tang, Xin Yan, Miao Jiang, Xu Zhao, Jie Chen, Caixia Jin, Qingjian Ou, Jingjun Zhao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.1field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
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  4. When ISG15 is involved in inflammation.Frontiers in immunology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Jiahui JinDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Yifan LiuDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Qinyu TangDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Xin YanDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Miao JiangDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Xu ZhaoDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Jie ChenDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Caixia JinDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Qingjian OuDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Jingjun ZhaoDepartment of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Tongji University · CNTongji Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Systemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific expressed hub genes associated with SSc, determine potential competing endogenous RNAs (ceRNA) regulatory networks, and identify potential targeted drugs. Methods: In this study, four datasets of SSc were acquired. To identify the genes specific to tissues or organs, the BioGPS web database was used. For differentially expressed genes (DEGs), functional and enrichment analyses were carried out, and hub genes were screened and shown in a network of protein-protein interactions (PPI). The potential lncRNA-miRNA-mRNA ceRNA network was constructed using the online databases. The specifically expressed hub genes and ceRNA network were validated in the SSc mouse and in normal mice. We also used the receiver operating characteristic (ROC) curve to determine the diagnostic values of effective biomarkers in SSc. Finally, the Drug-Gene Interaction Database (DGIdb) identified specific medicines linked to hub genes. Results: The pooled datasets identified a total of 254 DEGs. The tissue/organ-specifically expressed genes involved in this analysis are commonly found in the hematologic/immune system and bone/muscle tissue. The enrichment analysis of DEGs revealed the significant terms such as regulation of actin cytoskeleton, immune-related processes, the VEGF signaling pathway, and metabolism. Cytoscape identified six gene cluster modules and 23 hub genes. And 4 hub genes were identified, including Serpine1, CCL2, IL6, and ISG15. Consistently, the expression of Serpine1, CCL2, IL6, and ISG15 was significantly higher in the SSc mouse model than in normal mice. Eventually, we found that MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1 may be promising RNA regulatory pathways in SSc. Besides, ten potential therapeutic drugs associated with the hub gene were identified. Conclusions: This study revealed tissue-specific expressed genes, SERPINE1, CCL2, IL6, and ISG15, as effective biomarkers and provided new insight into the mechanisms of SSc. Potential RNA regulatory pathways, including MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1, contribute to our knowledge of SSc. Furthermore, the analysis of drug-hub gene interactions predicted TIPLASININ, CARLUMAB and BINDARIT as candidate drugs for SSc.

Indexed as

MicroRNAsRNA, Long NoncodingScleroderma, SystemicAnimalsBiomarkersComputational BiologyGene Expression ProfilingInterleukin-6MiceProtein Interaction MapsBiomarkersInterleukin-6MicroRNAsRNA, Long Noncodingbiomarkersdrug-gene interactionRNA regulatory pathwaysystemic sclerosistissue-specific expressed genes

Identifiers

PMID37063926
PMCPMC10098096
OpenAlexW4361274079

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.